.png){kind=icon}
.png){kind=icon}
Return
Optimized Test Scenario Identification Method based on Refined Warshall's Dynamic Programming for Validating Reinforcement Learning Model
.png){kind=icon}
PRE
AI
DOI：10.3837/tiis.2026.04.030.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In recent decades, Reinforcement learning (RL) has achieved remarkable performance in sequential decision-making tasks; however, validating RL-based software remains a significant challenge due to the exponential growth in the number of state-action combinations. We propose an optimized test-scenario identification method for a formal validation mechanism that integrates a refined Warshall's dynamic programming algorithm and path optimization to ensure efficient test coverage for RL systems. The method constructs a directed abstract graph from the RL model, applies transitive closure analysis to check reachability between states, and identifies missing states/transitions before testing. Our goal is to use test-scenario optimization to generate a minimal yet sufficient set of test cases, thereby achieving maximum coverage with minimal redundancy. This approach reduces verification complexity while maintaining mathematical rigor, making it well-suited for safety-critical applications such as autonomous driving. The proposed mechanism provides a scalable, interpretable validation process, offering a foundation for the reliable deployment of RL-based software in real-world systems.
Keywords:
Reinforcement Learning
Test Scenario Optimization
AI Software Validation
Journal
IF:
0.9
Papers:
240
Citations:
1.7K